The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications.

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

Chaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data. Through successful prediction studies conducted on the generated chaotic time series, forecasts can be made about events displaying unpredictable behavior in nature, which have not yet been modeled. This enables preparation for both favorable and unfavorable situations that may arise. In this study, chaotic time series were generated using Lorenz, Chen, and Rikitake multivariate chaotic systems. To enhance prediction accuracy on the generated data, GRU, LSTM and RNN models were trained with different hyperparameters. Subsequently, comprehensive test studies were conducted to evaluate their performance. Predictions were calculated using evaluation metrics, including MSE, RMSE, MAE, MAPE, and R2. In the experimental study, each chaotic system was trained with different hyperparameter combinations on six network models. The experimental results indicate that the utilized models exhibited greater success in predicting chaotic time series compared to some other models in the literature.